The Impact of a Hausman Pretest on the Size of Hypothesis Tests
AbstractThis paper investigates the size properties of a two-stage test in the linear instrumental variables model when in the first stage a Hausman (1978) specification test is used as a pretest of exogeneity of a regressor. In the second stage, a simple hypothesis about a component of the structural parameter vector is tested, using a t-statistic that is based on either the ordinary least squares (OLS) or the two-stage least squares estimator (2SLS) depending on the outcome of the Hausman pretest. The asymptotic size of the two-stage test is derived in a model where weak instruments are ruled out by imposing a lower bound on the strength of the instruments. The asymptotic size is a function of this lower bound and the pretest and second stage nominal sizes. The asymptotic size increases as the lower bound and the pretest size decrease. It equals 1 for empirically relevant choices of the parameter space. It is also shown that, asymptotically, the conditional size of the second stage test, conditional on the pretest not rejecting the null of regressor exogeneity, is 1 even for a large lower bound on the strength of the instruments. The size distortion is caused by a discontinuity of the asymptotic distribution of the test statistic in the correlation parameter between the structural and reduced form error terms. The Hausman pretest does not have sufficient power against correlations that are local to zero while the OLS t-statistic takes on large values for such nonzero correlations. Instead of using the two-stage procedure, the recommendation then is to use a t-statistic based on the 2SLS estimator or, if weak instruments are a concern, the conditional likelihood ratio test by Moreira (2003).
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Bibliographic InfoPaper provided by Cowles Foundation for Research in Economics, Yale University in its series Cowles Foundation Discussion Papers with number 1651.
Length: 45 pages
Date of creation: Apr 2008
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Publication status: Published in Journal of Econometrics (June 2010), 156(2): 337-343
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Find related papers by JEL classification:
- C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
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- Kevin E. Staub, 2009. "Simple tests for exogeneity of a binary explanatory variable in count data regression models," SOI - Working Papers 0904, Socioeconomic Institute - University of Zurich.
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